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  • Exploration of Supervised Machine Learning Techniques for Runtime Selection of CPU vs. GPU Execution in Java Programs. Gloria Kim, Akihiro Hayashi, Vivek Sarkar. Fourth Workshop on Accelerator Programming Using Directives (WACCPD), November 2017. (co-located with SC17)

  • Chapel-on-X: Exploring Tasking Runtimes for PGAS Languages. Akihiro Hayashi, Sri Raj Paul, Max Grossman, Jun Shirako, Vivek Sarkar. Third IEEE Workshop on Extreme Scale Programming Models and Middleware (ESPM2), November 2017. (co-located with SC17)

  • Deadlock Avoidance in Parallel Programs with Futures: Why parallel tasks should not wait for strangers. Tiago Cogumbreiro, Rishi Surendran, Francisco Martins, Vivek Sarkar, Vasco T. Vasconcelos, and Max Grossman. In ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM, 2017.


  • PIPES: A Language and Compiler for Task-Based Programming on Distributed-Memory Clusters. Martin Kong, Louis-Noël Pouchet, P. Sadayappan, Vivek Sarkar.  The Conference on High Performance Computing, Networking, Storage and Analysis (SC16), November 2016.
  • Static Cost Estimation for Data Layout Selection on GPUs. Yuhan Peng, Max Grossman, Vivek Sarkar. 7th International Workshop in Performance Modeling, Benchmarking, and Simulation of High Performance Computer Systems (PMBS16, co-located with SC16). November 2016.
  • Fine-grained parallelism in probabilistic parsing with Habanero Java. Matthew Francis-Landau (Johns Hopkins University), Bing Xue (Rice University), Jason Eisner (Johns Hopkins University), and Vivek Sarkar (Rice University). In Proceedings of the Sixth Workshop on Irregular Applications: Architectures and Algorithms (IA3, co-located with SC16), November 2016 [slides]. 
  • Exploring Compiler Optimization Opportunities for the OpenMP 4.x Accelerator Model on a POWER8+GPU PlatformAkihiro Hayashi, Jun Shirako, Ettore Tiotto, Robert Ho, Vivek Sarkar. Third Workshop on Accelerator Programming Using Directives (WACCPD, co-located with SC16), November 2016.

  • Optimized Distributed Work-Stealing. Vivek Kumar, Karthik Murthy, Vivek Sarkar and Yili Zheng. 6th workshop on Irregular Applications: Architectures and Algorithms (IA^3), ACM, November 2016 [slides]. 

  • Automatic Parallelization of Pure Method Calls via Conditional Future Synthesis. Rishi Surendran and Vivek Sarkar. 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA 2016), November 2016.

  • Pedagogy and Tools for Teaching Parallel Computing at the Sophomore Undergraduate Level. Max Grossman, Maha Aziz, Heng Chi, Anant Tibrewal, Shams Imam, Vivek Sarkar. Journal of Parallel and Distributed Computing Special Issue on Parallel, Distributed, and High Performance Computing Education. 2016. 










This material is based upon work supported by the National Science Foundation under Grants No. 0833166, 0938018, 0926127, 0964520, 1302570. Anyopinions,findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

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